线程池类似于多处理池？

是否有用于工作线程的Pool类，类似于多处理模块的Pool类？

我喜欢例如并行化地图功能的简单方法

def long_running_func(p):
    c_func_no_gil(p)

p = multiprocessing.Pool(4)
xs = p.map(long_running_func, range(100))

但是，我希望这样做而不会产生新流程的开销。

我知道GIL。但是，在我的用例中，该函数将是IO绑定的C函数，python包装程序将在实际函数调用之前为其释放GIL。

我必须编写自己的线程池吗？

我刚刚发现模块中实际上 有一个基于线程的Pool接口multiprocessing，但是它有些隐藏并且没有正确记录。

可以通过导入

from multiprocessing.pool import ThreadPool

它是使用包装Python线程的虚拟Process类实现的。可以找到基于线程的Process类multiprocessing.dummy，在docs中对其进行了简要介绍。该虚拟模块应该提供基于线程的整个多处理接口。

在Python 3中，您可以使用concurrent.futures.ThreadPoolExecutor，即：

executor = ThreadPoolExecutor(max_workers=10)
a = executor.submit(my_function)

有关更多信息和示例，请参阅文档。

是的，它似乎（或多或少）具有相同的API。

import multiprocessing

def worker(lnk):
    ....    
def start_process():
    .....
....

if(PROCESS):
    pool = multiprocessing.Pool(processes=POOL_SIZE, initializer=start_process)
else:
    pool = multiprocessing.pool.ThreadPool(processes=POOL_SIZE, 
                                           initializer=start_process)

pool.map(worker, inputs)
....

对于非常简单和轻巧的东西（从此处稍作修改）：

from Queue import Queue
from threading import Thread


class Worker(Thread):
    """Thread executing tasks from a given tasks queue"""
    def __init__(self, tasks):
        Thread.__init__(self)
        self.tasks = tasks
        self.daemon = True
        self.start()

    def run(self):
        while True:
            func, args, kargs = self.tasks.get()
            try:
                func(*args, **kargs)
            except Exception, e:
                print e
            finally:
                self.tasks.task_done()


class ThreadPool:
    """Pool of threads consuming tasks from a queue"""
    def __init__(self, num_threads):
        self.tasks = Queue(num_threads)
        for _ in range(num_threads):
            Worker(self.tasks)

    def add_task(self, func, *args, **kargs):
        """Add a task to the queue"""
        self.tasks.put((func, args, kargs))

    def wait_completion(self):
        """Wait for completion of all the tasks in the queue"""
        self.tasks.join()

if __name__ == '__main__':
    from random import randrange
    from time import sleep

    delays = [randrange(1, 10) for i in range(100)]

    def wait_delay(d):
        print 'sleeping for (%d)sec' % d
        sleep(d)

    pool = ThreadPool(20)

    for i, d in enumerate(delays):
        pool.add_task(wait_delay, d)

    pool.wait_completion()

要支持完成任务的回调，您只需将回调添加到任务元组即可。

嗨，在Python中使用线程池可以使用以下库：

from multiprocessing.dummy import Pool as ThreadPool

然后使用，这个库就是这样的：

pool = ThreadPool(threads)
results = pool.map(service, tasks)
pool.close()
pool.join()
return results

线程是所需的线程数，任务是大多数映射到服务的任务列表。

这是我最终使用的结果。它是上述dgorissen类的修改版本。

文件： threadpool.py

from queue import Queue, Empty
import threading
from threading import Thread


class Worker(Thread):
    _TIMEOUT = 2
    """ Thread executing tasks from a given tasks queue. Thread is signalable, 
        to exit
    """
    def __init__(self, tasks, th_num):
        Thread.__init__(self)
        self.tasks = tasks
        self.daemon, self.th_num = True, th_num
        self.done = threading.Event()
        self.start()

    def run(self):       
        while not self.done.is_set():
            try:
                func, args, kwargs = self.tasks.get(block=True,
                                                   timeout=self._TIMEOUT)
                try:
                    func(*args, **kwargs)
                except Exception as e:
                    print(e)
                finally:
                    self.tasks.task_done()
            except Empty as e:
                pass
        return

    def signal_exit(self):
        """ Signal to thread to exit """
        self.done.set()


class ThreadPool:
    """Pool of threads consuming tasks from a queue"""
    def __init__(self, num_threads, tasks=[]):
        self.tasks = Queue(num_threads)
        self.workers = []
        self.done = False
        self._init_workers(num_threads)
        for task in tasks:
            self.tasks.put(task)

    def _init_workers(self, num_threads):
        for i in range(num_threads):
            self.workers.append(Worker(self.tasks, i))

    def add_task(self, func, *args, **kwargs):
        """Add a task to the queue"""
        self.tasks.put((func, args, kwargs))

    def _close_all_threads(self):
        """ Signal all threads to exit and lose the references to them """
        for workr in self.workers:
            workr.signal_exit()
        self.workers = []

    def wait_completion(self):
        """Wait for completion of all the tasks in the queue"""
        self.tasks.join()

    def __del__(self):
        self._close_all_threads()


def create_task(func, *args, **kwargs):
    return (func, args, kwargs)

使用游泳池

from random import randrange
from time import sleep

delays = [randrange(1, 10) for i in range(30)]

def wait_delay(d):
    print('sleeping for (%d)sec' % d)
    sleep(d)

pool = ThreadPool(20)
for i, d in enumerate(delays):
    pool.add_task(wait_delay, d)
pool.wait_completion()

创建新流程的开销非常小，尤其是其中只有4个时。我怀疑这是您应用程序的性能热点。保持简单，优化您必须去的地方以及分析结果指向的地方。

没有基于线程的内置池。但是，用Queue该类实现生产者/消费者队列可能很快。

来自：https : //docs.python.org/2/library/queue.html

from threading import Thread
from Queue import Queue
def worker():
    while True:
        item = q.get()
        do_work(item)
        q.task_done()

q = Queue()
for i in range(num_worker_threads):
     t = Thread(target=worker)
     t.daemon = True
     t.start()

for item in source():
    q.put(item)

q.join()       # block until all tasks are done